Removing environment factors from signals generated from video images captured for biomedical measurements

ABSTRACT

What is disclosed is a system and method for automatically removing undesirable periodic or random background noise from heart rate measurement signals obtained from a video camera, ambient illuminator and other unknown electromagnetic sources to improve the overall reliability of biomedical measurements. In one embodiment, a time varying video image acquired over at least one imaging channel of a subject of interest is received. The video images are then segmented into a first region comprising a localized area where plethysmographic signals of the subject can be registered and a second region comprising a localized area of the environment where the plethysmographic signals cannot be registered. Both of the regions are exposed to the same environmental factors. The segmented video signals are pre-processed and the processed signals are subtracted from each other to generate an environmentally compensated signal. The environmentally compensated signal is then communicated to a computer system.

TECHNICAL FIELD

The present invention is directed to systems and methods for removingundesirable signals and background noise from signals generated fromvideo images captured using a RGB camera or an Infrared (IR) camera forimproved accuracy and reliability of biomedical measurements derivedfrom those images.

BACKGROUND

Current Electro-Cardio Graphic (ECG) systems require the patient to belocated in close proximity to the ECG machine obtaining measurements viaelectrodes attached to the skin. The adhesive electrodes can cause skinirritation, infection, discomfort, and other issues to the patient. Thiscan especially be a problem to newborns with sensitive skin. Methods fornon-contact cardiac pulse measurement based on imaging patients usingRGB and/or multi-spectral infrared (IR) cameras have arisen in this art.By recording video images of the region of exposed skin whereconcentrations of blood vessels exist, small changes in pulsating insideblood vessels are registered as blood volume signals on detector arrays.These signals can comprise a mixture of patient plethysmographic signals(i.e., blood volume signals) along with other artifacts from theenvironment. The detector arrays also may register involuntary andvoluntary bodily motions and muscle fluctuations. Biomedical signals canbe corrupted by fluctuations in illumination source, electronic powerline noise, periodic signals manifested by camera auto calibration, andthe like. Unwanted signals are difficult to separate from desiredsignals when these have frequency components that are within thebandwidth of the frequency of the human heart rate. Therefore, a needexists to automatically compensate video images to enhance the signalquality required during estimation.

Accordingly, what is needed in this art are sophisticated systems andmethods for removing undesirable periodic signals and random backgroundnoise from video images obtained from a RGB camera or an infrared (IR)camera for improved accuracy and reliability of biomedical measurementsobtained from those captured signals.

INCORPORATED REFERENCES

The following U.S. patents, U.S. patent applications, and Publicationsare incorporated herein in their entirety by reference.

-   “Estimating Cardiac Pulse Recovery From Multi-Channel Source Data    Via Constrained Source Separation”, U.S. Pat. No. 8,617,081.-   “Filtering Source Video Data Via Independent Component Selection”,    U.S. Pat. No. 8,600,213.-   “Blind Signal Separation: Statistical Principles”, Jean-Francois    Cardoso, Proceedings of the IEEE, Vol. 9, No. 10, pp. 2009-2025,    (October 1998).-   “Independent Component Analysis: Algorithms And Applications”, Aapo    Hyvärinen and Erkki Oja, Neural Networks, 13(4-5), pp. 411-430,    (2000).-   “Infrared Thermal Imaging: Fundamentals, Research and Applications”,    Michael Vollmer, Klaus Peter Möllmann, Wiley-VCH; 1^(st) Ed. (2010)    ISBN-13: 978-3527407170.

BRIEF SUMMARY

What is disclosed is a system and method for removing undesirableperiodic signals and random background noise from signals generated fromvideo images captured from a RGB or infrared (IR) camera for improvedaccuracy and reliability of biomedical measurements.

One embodiment of the present system and method for removingenvironmental factors from video images captured by a non-contactimaging system involves the following. First, video images are capturedof a subject of interest. The video comprises a time varying sourcevideo images acquired over at least one imaging channel. The acquiredsource signal can be any combination of: NIR signals, RGB signals,multi-spectral signals, and hyperspectral signals. The video images aresegmented into two regions of interest, i.e., a first region being alocalized area where plethysmographic signals of the subject can beregistered, and a second region being a localized area of theenvironment where plethysmographic signals cannot be registered. Both ofthese regions have been exposed to the same environmental factorscontaining undesirable environmental factors such as periodic signalsand random background noise. The segmented video images for each of thefirst and second ROIs are pre-processed by performing various imagepre-processing steps to generate time-series signals and further sourceseparation with blind source separation or by a constrained sourceseparation. The pre-processed signals corresponding to each of theimaging channels are subtracted to generate correspondingenvironmentally compensated signals. The environmentally compensatedsignals are then communicated to a computing system to extractplethysmographic signals.

Many features and advantages of the above-described method will becomereadily apparent from the following detailed description andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the subject matterdisclosed herein will be made apparent from the following detaileddescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 shows a video image captured of a subject of interest;

FIG. 2 shows the image of FIG. 1 with a first localized area 201identified where the subject's plethysmographic signals are registered,and a second localized area 202 where those same signals are registered,i.e., a background area of the image;

FIG. 3 is a flow diagram of one example embodiment of the present methodfor removing environmental factors from signals generated from videoimages captured by a non-contact imaging system in a remote sensingenvironment;

FIG. 4 is a block diagram of an example networked video image processingsystem wherein various aspects of the present method as described withrespect to the flow diagram of FIG. 3 are implemented;

FIG. 5A plots the spectral content of a second imaging channel(post-ICA) of the imaging system used to acquire the source video imagesto isolate the components present in the segmented video images of thelocalized area 201 of FIG. 2;

FIG. 5B plots the spectral content of a second imaging channel(post-ICA) of the imaging system used to acquire the source video imagesto isolate the signal components in the segmented source video images ofthe localized area 202 of FIG. 2 where the subject's plethysmographicsignal cannot be registered;

FIGS. 6A-C shows the power spectral density of the signals acquired forall three imaging channels before performing signal compensation; and

FIGS. 7A-C shows the power spectral density for the signals of the threeimaging channels after compensating the source video image according tothe teachings hereof.

DETAILED DESCRIPTION

What is disclosed is a system and method for removing undesirableperiodic signals and random background noise from video images obtainedfrom a RGB or multi-spectral IR camera for improved accuracy andreliability of biomedical measurements obtained from those capturedsignals.

It should be understood that one of ordinary skill in this art would bereadily familiar with advance mathematical techniques involving matrixmethods, independent component analysis, and data projection. One ofordinary skill would be familiar with the texts, “Independent ComponentAnalysis”, Wiley-Interscience, 1^(st) Ed. (2001), ISBN-13:978-0471405405, and “Independent Component Analysis: Principles andPractice”, Cambridge University Press; 1^(st) Ed. (2001), ISBN-13:978-0521792981, which are incorporated herein in their entirety byreference.

Non-Limiting Definitiions

A “subject of interest”, as used herein, refers to a subject capable ofregistering a plethysmographic signal. FIG. 1 shows an example image 100of a video taken of a subject of interest 102 for processing inaccordance with the teachings hereof. Use of the term “human”, or“person, or “patient” herein for explanatory purposes is not to beviewed as limiting the scope of the appended claims solely to humanbeings. The present method applies equally to other biological subjectscapable of registering a plethysmographic signal in a captured videoimage such as mammals, birds, fish, reptiles, and certain insects.

A “plythesmographic signal” is a signal which contains meaningful dataas to a physiological change in pulsating blood volume or volumetricpressure of the localized area of the subject intended to be analyzed.Pulmonary plethysmography measures the volume in the subject's lungs,i.e., lung volume. Plythesmography of the limbs helps determinecirculatory capacity. Penile plethysmography measures changes in bloodflow in the penis. Whole-body plethysmography helps practitionersmeasure a variety of parameters in their patients.

An “imaging sensor” is a device for capturing source video data over oneor more channels of a subject of interest. The imaging sensor may be adevice with a high frame rate and high spatial resolution such as, forexample, a monochrome camera for capturing black/white video images, ora color camera for capturing color video images. The imaging sensor maybe a spectral sensor such as a multi-spectral or hyperspectral system.Spectral sensors are devices which have relatively low frame rates andlow spatial resolution but high spectral resolution. The imaging sensormay be a hybrid device capable of operating in a conventional video modewith high frame rate and high spatial resolution, and a spectral modewith low frame rates but high spectral resolution. Imaging sensorscomprising standard video cameras and those comprising spectral sensorsare readily available from many vendors in various streams of commerce.

A “source video image” is the time varying video image acquired using animaging sensor. A source video image can be any combination of: NIRimages, RGB images, RGB and NIR images, multi-spectral images, andhyperspectral video images.

A “time-series signal” is time varying signal obtained from the 2D videoimages by transforming to 1D during pre-processing.

“Segmenting the video image” means identifying, in the video images, afirst region of interest comprising a localized area where the subject'splethysmographic signals can be registered (area 201 of image 200 ofFIG. 2) and identifying a second region of interest comprising alocalized area where the subject's plethysmographic signals cannot beregistered (area 202). The localized areas do not have to be the samesize, but both areas in the image need to have been exposed to the sameenvironmental factors. Environmental factors include fluctuations inillumination source, electronic power line noise, periodic signalsinduced by the imaging system, and the like, which induce undesirableperiodic signals and random background noise in the video. As discussedin the background section hereof, undesirable signals and backgroundnoise are difficult to separate from desired signals of interest whenthe undesirable signals have frequency components that are within thebandwidth of the frequency of the subject's plethysmographic signalsintended to be accurately acquired for biomedical measurements. Theteachings hereof are directed to processing video images such that thequality of the desired signals of interest is enhanced to improve theaccuracy of the biomedical measurements derived therefrom.

“Subtracting the pre-processed signals” means subtracting the signalsgenerated from pre-processed video images of the segmented region whichdoes not contain the subject's plethysmographic signals from the signalsgenerated from pre-processed video images which do contain the subject'splethysmographic signals (corresponding to each of the imaging channelsused to acquire the source signals) to generate, for each channel, anenvironmentally compensated signal.

“Independent Component Analysis” (ICA) is a decomposition method foruncovering independent source signal components from a set ofobservations that are composed of linear mixtures of the underlyingsources, called “independent components” of the observed data. ICAdefines a generative model for the observed multivariate data, which istypically given as a large database of samples. In the model, the datavariables are assumed to be linear mixtures of some unknown latentvariables, and the mixing system is also unknown. The latent variablesare assumed non-Gaussian and mutually independent, and they are calledthe independent components of the observed data. These independentcomponents, also called sources or factors, can be found by ICA. ICA issuperficially related to principal component analysis and factoranalysis. ICA is a much more powerful technique, however, capable offinding the underlying factors or sources when these classic methodsfail completely. The data analyzed by ICA could originate from manydifferent kinds of application fields, including digital images,databases, psychometric measurements. In many cases, the measurementsare given as a set of parallel signals or time-series. ICA is one formof blind source separation.

“Blind Source Separation” (BSS) is a technique for the recovery ofunobserved source signals from a set of observed mixed signals withoutany prior information being known about the “mixing” process.

A “remote sensing environment” refers to non-contact, non-invasivesensing, i.e., the imaging sensor does not physically contact thesubject being sensed. The environment may be any settings such as, forexample, a hospital, ambulance, medical office, and the like.

Flow Diagram of One Example Embodiment

Reference is now being made to FIG. 3 which is a flow diagram of oneexample embodiment of the present method for removing environmentalfactors from signals generated from video images captured by anon-contact imaging system in a remote sensing environment. Flowprocessing begins at step 300 and immediately proceeds to step 302.

At step 302, receive video images captured of the subject of interestusing an imaging sensor. The video images are preprocessed to compensatefor motion blur, slow illumination variation induced colorinconsistency, and any geometric distortion.

At step 304, segment the video images into at least two regions ofinterest with a first region of interest comprising a localized areawhere the subject's plethysmographic signals can be registered (such asthe localized area of exposed skin 201 of FIG. 1, based on color,material, spatial features, and the like), and a second region ofinterest comprising the surrounding background environment where thesubject's plethysmographic signals cannot be registered (such aslocalized background area 202 of FIG. 1). In advance of segmenting thevideo, the source signal may be processed to compensate for motioninduced blur, imaging blur, and/or slow illuminant variation. Thiscompensation is preferably carried out in a time-domain beforeperforming a Fourier transform.

At step 306, pre-process the video images for each of the first andsecond regions of interest. Pre-processing includes at least one of asource separation with blind source separation, and/or a constrainedsource separation. In various embodiments, pre-processing includesperforming, for each of the first and second regions, the followingsteps: 1) average the value of all pixels in this channel to obtain achannel average per image frame; 2) compute a global channel average anda global standard deviation; 3) subtract the channel average from theglobal channel average to produce a resulting signal; 4) divide theresulting signal by the standard deviation to obtain a zero-mean unitvariance time-series signal; 5) normalize the time-series signal; 6)band pass filter the normalized time-series signal to remove undesirablefrequencies which are below and above the expected frequencies of thesubject; and 7) perform signal whitening. A Fourier Transform (or anyother spectral analysis techniques such as Auto-regression Model) may beperformed on the source signal to remove periodic noise in advance ofperforming the subtraction of step 308. It is to be noted that a sortingor phase problem may arise while processing each regions with the blindsource separation method such as the independent component analysiswhich should be resolved prior to subtraction of source separatedsignals. Alternately, if sorting or phase problems persist then sourceseparation can be carried out on the subtracted signals after performingsignal whitening.

At step 308, subtract the pre-processed signal of the region containingthe localized area of the surrounding background environment from thepre-processed signal of the region containing the subject'splethysmographic signals. This generates an environmentally compensatedsignal, for each channel.

At step 310, communicate the environmentally compensated signal to acomputer system. In this embodiment, further processing stops.

In another embodiment, a set of reference signals having a frequencyrange which approximates a frequency range of the subject's cardiacpulse are generated. Then, using the reference signal, a constrainedsource separation is performed on the subtracted signals to obtain anestimated source signal with a minimum error. The minimum error isachieved by adjusting the phase of the estimated source signal andcalculating a difference between the two waveforms. A cardiac frequencyof the subject is then estimated based upon a frequency at which theminimum error was achieved. One or more aspects of the reference signalcan be modified by changing a frequency, amplitude, phase, or the waveform of the reference signal where the wave form is a sine wave, asquare wave, a user defined shape such as that obtained from an ECGsignal, or a cardiac pulse wave form derived from the subject.

Example Signal Processing System

Reference is now being made to FIG. 4 which is a block diagram of anexample networked video image processing system wherein various aspectsof the present method as described with respect to the flow diagram ofFIG. 3 are implemented.

In FIG. 4, imaging sensor 402 acquires source video images of a subjectof interest in the sensor's field of view 403 over at least one imagingchannel. The source video images are communicated to Video ImageProcessing System 404 wherein various aspects of the present method areperformed. The example image processing system is shown comprising aBuffer 406 for buffering frames of the source video image forprocessing. Buffer 406 may further store data, formulas and othermathematical representations as are necessary to process the sourcevideo images in accordance with the teachings hereof. Image StabilizerModule 408 is provided to process the images to compensate for motioninduced blur, imaging blur, slow illuminant variation, and the like.Video Image Processor 410 segments the video images into signals of afirst localized area (such as localized area 201 of FIG. 2) where thesubject's plethysmographic signals can be registered, and a secondlocalized area (such as localized area 202 of FIG. 2) where thesubject's plethysmographic signals cannot be registered. One or moreframes of the source video captured of the subject of interest may bedisplayed on a display device such that the user or operator can selectany of the first and second localized areas using, for example, arubber-band box generated by a mouse-over operation. Source video imagesassociated with each of the identified localized areas, for each of theacquiring channels, are provided to Video Image Pre-Processor 412 whichreceives the segmented source images of each localized area,pre-processes the segmented source images, converts to time-seriessignal and identifies the components of those signals by havingperformed a source separation with blind source separation, or aconstrained source separation on the signals of each of the segmentedregions for each imaging channel used to acquire those source images. Ifthe sorting and phase problem cannot be fully resolved then sourceseparation is performed after Signal Comparator 418. Various signalcomponents may be stored/retrieved to storage device 416 usingcommunication pathways not shown. Signal Comparator 418 receives thepre-processed signals for each region for each channel and subtracts thetwo signals from each other. A result of the subtraction is anenvironmentally compensated signal 420. Signal Communication Link 422receives the signal 420 and provides the environmentally compensatedsignal to one or more remote devices via Transmission Antenna 424.Network Link 422 further provides the environmentally compensated signal420 to computer system 428. Data is transferred between devices in anetwork in the form of signals which may be in any combination ofelectrical, electro-magnetic, optical, or other forms. Such signals aretransmitted via wire, cable, fiber optic, phone line, cellular link, RF,satellite, or any other medium known in the arts.

In the embodiment shown, computer system 428 comprises a workstation.Networked workstation 428 includes a hard drive (internal to computercase 442) which reads/writes to computer readable media 440 such as afloppy disk, optical disk, CD-ROM, DVD, magnetic tape, etc. Case 442also houses a motherboard with a processor and memory, a network card,graphics card, and the like, and other software and hardware. Theworkstation includes a user interface which comprises display 432 suchas a CRT, LCD, touch screen, etc., mouse 435, and keyboard 434. Itshould be appreciated that the workstation has an operating system andother specialized software configured to display a variety of numericvalues, text, scroll bars, pull-down menus with user selectable options,and the like, for entering, selecting, or modifying informationdisplayed on display device 432. Various portions of the source videosignals captured by sensor 402 may be communicated to workstation 428for processing. It should be appreciated that some or all of thefunctionality performed by any of the modules and processing units ofthe signal processing system 404 can be performed, in whole or in part,by workstation 428. Workstation 428 is in communication with network 430via a communications interface (not shown). A user or technician may usethe keyboard 434 and mouse 436, to identify regions of interest, setparameters, select images for processing, view results, and the like.Any of these may be stored to storage device 438 or written to computermedia 440 such as, for example, a CD-ROM drive, using a read/writedevice located in computer case 442. Any of the modules and processingunits of FIG. 4 can be placed in communication with device 416 and maystore/retrieve therefrom data, variables, records, parameters,functions, machine readable/executable program instructions required toperform their intended functions. Moreover each of the modules of system404 may be placed in communication with one or more devices over network430. Although shown as a desktop computer, it should be appreciated thatcomputer system 428 can be any of a laptop, mainframe, server, or aspecial purpose computer such as an ASIC, circuit board, dedicatedprocessor, or the like.

Performance Results

Tests were performed using a 3 channel RGB video camera which produces asource video images containing camera-induced noise. Attention isdirected to FIG. 5A which plots the power spectral density of the secondchannel from the segmented source video images of the localized area 201of FIG. 2. Two dominant components are present, i.e., a first dominantsignal component (at 501) comprises the subject's plethysmographicsignal (at approximately 56 beats per minute (bpm)), and a seconddominant component (at 502) comprises the undesirable camera-inducednoise centered about 120 bpm. FIG. 5B plots the power spectral densityof the same second channel (post-ICA) from the segmented source videoimages of the localized area 202 of FIG. 2 where the subject'splethysmographic signal cannot be registered. Notice that the subject'splethysmographic signal (around 56 bpm) does not appear indicating thenon-existence of those signals in the background environment but thatthe localized area 202 does contain the undesirable signal (at 503)centered about 120 bpm.

FIGS. 6A-C shows the power spectral density of the signals acquired forall three imaging channels before performing signal compensation asdisclosed herein. FIGS. 7A-C shows the power spectral density for thesignals of the three imaging channels after compensating the sourcevideo signal according to the teachings hereof. As shown, theundesirable camera-induced noise around 120 bpm (present in each of thethree imaging channels) has been effectively eliminated while thesubject's plethysmographic signal (dominant in the second channel) islargely retained. These clearly demonstrate the viability of theteachings disclosed herein. Moreover, various random signals on eitherside of subject's plethysmographic signal have been reduced as well.

Various Embodiments

It should also be appreciated that various modules may designate one ormore components which may, in turn, comprise software and/or hardwaredesigned to perform the intended function. A plurality of modules maycollectively perform a single function. Each module may have aspecialized processor capable of executing machine readable programinstructions. A module may comprise a single piece of hardware such asan ASIC, electronic circuit, or special purpose processor. A pluralityof modules may be executed by either a single special purpose computersystem or a plurality of special purpose computer systems operating inparallel. Connections between modules include both physical and logicalconnections. Modules may further include one or more software/hardwaremodules which may further comprise an operating system, drivers, devicecontrollers, and other apparatuses some or all of which may be connectedvia a network. It is also contemplated that one or more aspects of thepresent method may be implemented on a dedicated computer system and mayalso be practiced in distributed computing environments where tasks areperformed by remote devices that are linked through a network. Theteachings hereof can be implemented in hardware or software using anyknown or later developed systems, structures, devices, and/or softwareby those skilled in the applicable art without undue experimentationfrom the functional description provided herein with a general knowledgeof the relevant arts.

One or more aspects of the methods described herein are intended to beincorporated in an article of manufacture, including one or morecomputer program products, having computer usable or machine readablemedia. For purposes hereof, a computer usable or machine readable mediais, for example, a floppy disk, a hard-drive, memory, CD-ROM, DVD, tape,cassette, or other digital or analog media, or the like, which iscapable of having embodied thereon a computer readable program, one ormore logical instructions, or other machine executable codes or commandsthat implement and facilitate the function, capability, andmethodologies described herein. Furthermore, the article of manufacturemay be included on at least one storage media readable by a machinearchitecture or image processing system embodying executable programinstructions capable of performing the methodology described in the flowdiagrams. The article of manufacture may be included as part of anoperating system, a plug-in, or may be shipped, sold, leased, orotherwise provided separately, either alone or as part of an add-on,update, upgrade, or product suite.

It will be appreciated that various of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be desirablycombined into many other different systems or applications. Variouspresently unforeseen or unanticipated alternatives, modifications,variations, or improvements therein may become apparent and/orsubsequently made by those skilled in the art, which are also intendedto be encompassed by the following claims. Accordingly, the embodimentsset forth above are considered to be illustrative and not limiting.Various changes to the above-described embodiments may be made withoutdeparting from the spirit and scope of the invention. The teachings ofany printed publications including patents and patent applications, areeach separately hereby incorporated by reference in their entirety.

What is claimed is:
 1. A method for removing environmental factors from signals generated from video images captured by a non-contact imaging system in a remote sensing environment, the method comprising: receiving video images captured of a subject of interest, said video comprising a time varying source video image acquired over at least one imaging channel; segmenting said video images into a first region of interest comprising a localized area where plethysmographic signals of said subject can be registered and a second region of interest comprising a background area, both of said first and second regions being exposed to the same environmental factors; pre-processing said video images to generate a time-series signal for each of said first and second regions; subtracting said time-series signal corresponding to each of said first and second regions to obtain an environmentally compensated signal; and communicating said environmentally compensated signal to a computer system.
 2. The method of claim 1, wherein pre-processing said video images is performed using at least one of: a source separation with blind source separation, and a constrained source separation.
 3. The method of claim 2, wherein said pre-processing further comprises, for each channel: computing an average value of all pixels acquired with this channel to obtain a channel average per image frame; computing a global channel average and a global standard deviation from said computed averages for this channel; subtracting said channel average from said global channel average to produce a resulting signal; dividing said resulting signal by said standard deviation to obtain a zero-mean unit variance time-series signal for said region; normalizing said time-series signal; and band-pass filtering said normalized time-series signal to remove frequencies that are above and below expected frequencies of the plethysmographic signals of said subject.
 4. The method of claim 1, wherein said acquired source video image comprises any combination of: NIR images, RGB images, RGB and NIR images, multi-spectral images, and hyperspectral video images.
 5. The method of claim 1, wherein, in advance of segmenting said video images, further comprising compensating for any of: a motion induced blur, an imaging blur, and slow illuminant variation.
 6. The method of claim 5, wherein said compensation is carried out in a time-domain before performing a Fourier transform such that both a periodic and a non-periodic background signal can be reduced.
 7. The method of claim 1, further comprising performing a Fourier Transform on said source signal to remove periodic noise in advance of said subtraction.
 8. The method of claim 1, further comprising: generating a set of reference signals having a frequency range which approximates a frequency range of said subject's cardiac pulse; performing, using said reference signal, a constrained source separation on said source data to obtain an estimated source signal with a minimum error; and estimating a cardiac frequency of said subject based upon a frequency at which said minimum error was achieved.
 9. The method of claim 8, wherein said minimum error was achieved by adjusting phase of the estimated source signal and calculating a difference between two waveforms.
 10. The method of claim 8, further comprising changing at least one aspect of said reference signal by changing any of: a frequency, an amplitude, a phase, and a wave form of said reference signal.
 11. The method of claim 10, wherein said wave form comprises any of: a sine wave, a square wave, a user defined shape such as that obtained from an ECG signal, and a cardiac pulse wave form derived from said subject.
 12. A system for removing environmental factors from signals generated from video images captured by a non-contact imaging system in a remote sensing environment, the system comprising: an imaging sensor for acquiring a time varying source video image acquired over at least one imaging channel; and a processor in communication with said imaging sensor and a memory, said processor executing machine readable instructions for performing: receiving video images captured of a subject of interest using said sensor; segmenting said video images into a first region of interest comprising a localized area where plethysmographic signals of said subject can be registered and a second region of interest comprising a background area, both of said first and second regions being exposed to the same environmental factors; pre-processing said video images to generate a time-series signal for each of said first and second regions; subtracting said time-series signal corresponding to each of said first and second regions to obtain an environmentally compensated signal; and communicating said environmentally compensated signal to a computer system.
 13. The system of claim 12, wherein pre-processing said video images is performed using at least one of: a source separation with blind source separation, and a constrained source separation.
 14. The system of claim 13, wherein said pre-processing further comprises, for each channel: computing an average value of all pixels acquired with this channel to obtain a channel average per image frame; computing a global channel average and a global standard deviation from said computed averages for this channel; subtracting said channel average from said global channel average to produce a resulting signal; dividing said resulting signal by said standard deviation to obtain a zero-mean unit variance time-series signal for said region; normalizing said time-series signal; and band-pass filtering said normalized time-series signal to remove frequencies that are above and below expected frequencies of the plethysmographic signals of said subject.
 15. The system of claim 12, wherein said acquired source video image comprises any combination of: NIR images, RGB images, RGB and NIR images, multi-spectral images, and hyperspectral video images.
 16. The system of claim 12, wherein, in advance of segmenting said video images, further comprising compensating for any of: a motion induced blur, an imaging blur, and slow illuminant variation.
 17. The system of claim 16, wherein said compensation is carried out in a time-domain before performing a Fourier transform such that both a periodic and a non-periodic background signal can be reduced.
 18. The system of claim 12, further comprising performing a Fourier Transform on said source signal to remove periodic noise in advance of said subtraction.
 19. The system of claim 12, further comprising: generating a set of reference signals having a frequency range which approximates a frequency range of said subject's cardiac pulse; performing, using said reference signal, a constrained source separation on said source data to obtain an estimated source signal with a minimum error; and estimating a cardiac frequency of said subject based upon a frequency at which said minimum error was achieved.
 20. The system of claim 19, wherein said minimum error was achieved by adjusting phase of the estimated source signal and calculating a difference between two waveforms.
 21. The system of claim 19, further comprising changing at least one aspect of said reference signal by changing any of: a frequency, an amplitude, a phase, and a wave form of said reference signal.
 22. The system of claim 21, wherein said wave form comprises any of: a sine wave, a square wave, a user defined shape such as that obtained from an ECG signal, and a cardiac pulse wave form derived from said subject.
 23. A computer implemented method for removing environmental factors from signals generated from video images captured by a non-contact imaging system in a remote sensing environment, the method comprising: receiving video images captured of a subject of interest, said video comprising a time varying source video image acquired over at least one imaging channel, said acquired source video image comprising any combination of: NIR images, RGB images, RGB and NIR images, multi-spectral images, and hyperspectral video images; segmenting said video images into a first region of interest comprising a localized area where plethysmographic signals of said subject can be registered and a second region of interest comprising a background area, both of said first and second regions being exposed to the same environmental factors; pre-processing said video images to generate a normalized time-series signal for each of said first and second regions, said pre-processing including performing at least one of: a source separation with blind source separation, and a constrained source separation; subtracting said normalized time-series signal corresponding to each of said first and second regions to obtain an environmentally compensated signal; and communicating said environmentally compensated signal to a computer system.
 24. The computer implemented method of claim 23, wherein said pre-processing further comprises, for each channel: computing an average value of all pixels acquired with this channel to obtain a channel average per image frame; computing a global channel average and a global standard deviation from said computed averages for this channel; subtracting said channel average from said global channel average to produce a resulting signal; dividing said resulting signal by said standard deviation to obtain a zero-mean unit variance time-series signal for said region; normalizing said time-series signal; and band-pass filtering said normalized time-series signal to remove frequencies that are above and below expected frequencies of the plethysmographic signals of said subject.
 25. The computer implemented method of claim 23, further comprising: generating a set of reference signals having a frequency range which approximates a frequency range of said subject's cardiac pulse; performing, using said reference signal, a constrained source separation on said source data to obtain an estimated source signal with a minimum error, said minimum error being achieved by adjusting phase of the estimated source signal and calculating a difference between two waveforms; and estimating a cardiac frequency of said subject based upon a frequency at which said minimum error was achieved. 